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uWS-MR is a software solution intended to be used for viewing, manipulation, and storage of medical images. It supports interpretation and evaluations within healthcare institutions. It has the following additional indications:

The MR Stitching is intended to create full-format images from overlapping MR volume data sets acquired at multiple stages.

The Dynamic application is intended to provide a general post-processing tool for time course studies.

The Image Fusion application is intended to combine two different image series so that the displayed anatomical structures match in both series.

MRS (MR Spectroscopy) is intended to evaluate the molecule constitution and spatial distribution of cell metabolism. It provides a set of tools to view, process, and analyze the complex MRS data. This application supports the analysis for both SVS (Single Voxel Spectroscopy) and CSI (Chemical Shift Imaging) data.

The MAPs application is intended to provide a number of arithmetic and statistical functions for evaluating dynamic processes and images. These functions are applied to the grayscale values of medical images.

The MR Breast Evaluation application provides the user a tool to calculate parameter maps from contrast-enhanced timecourse images.

The Brain Perfusion application is intended to allow the visualizations in the dynamic susceptibility time series of MR datasets.

MR Vessel Analysis is intended to provide a tool for viewing, manipulating MR vascular images. The Inner view application is intended to perform a virtual camera view through hollow structures (cavities), such as vessels.

uWS-MR is a comprehensive software solution designed to process, review and analyze MR (Magnetic Resonance Imaging) studies. It can transfer images in DICOM 3.0 format over a medical imaging network or import images from external storage devices such as CD/DVDs or flash drives. These images can be functional data, as well as anatomical datasets. It can be at one or more time-points or include one or more time-frames. Multiple display formats including MIP and volume rendering and multiple statistical analysis including mean, maximum and minimum over a user-defined region is supported. A trained, licensed physician can interpret these displayed images as well as the statistics as per standard practice.

The provided document is a 510(k) premarket notification for the uWS-MR software. It focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a standalone study with detailed acceptance criteria and performance metrics for a novel AI-powered diagnostic device.

Therefore, much of the requested information regarding specific acceptance criteria, detailed study design for proving the device meets those criteria, sample sizes for test and training sets, expert qualifications, and ground truth establishment cannot be fully extracted from this document.

Here's what can be inferred and stated based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list quantitative acceptance criteria in a pass/fail format nor provide specific, measurable performance metrics for the proposed device (uWS-MR) in comparison to such criteria. Instead, it relies on demonstrating substantial equivalence to predicate devices by comparing their features and functionalities. The "Remark" column consistently states "Same," indicating that the proposed device's features align with its predicates, implying it meets comparable performance.

2. Sample Size Used for the Test Set and the Data Provenance

The document states: "Software verification and validation testing was provided to demonstrate safety and efficacy of the proposed device." and lists "Performance Verification" for various applications. However, it does not specify the sample size used for these performance tests (test set) or the data provenance (e.g., country of origin, retrospective/prospective nature).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This information is not provided in the document. The filing focuses on technical and functional equivalence, not on clinical performance evaluated against expert ground truth.

4. Adjudication Method

This information is not provided in the document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned as being performed or required. The submission is a 510(k) for substantial equivalence, which often relies more on technical verification and comparison to predicate devices rather than new clinical effectiveness studies.

6. Standalone Performance

The document clearly states: "Not Applicable to the proposed device, because the device is stand-alone software." This implies that the device is intended to perform its functions (viewing, manipulation, post-processing) as a standalone software, and its performance was evaluated in this standalone context during verification and validation, aligning with the predicates which are also software solutions. However, no specific standalone performance metrics are provided.

7. Type of Ground Truth Used

The document does not explicitly state the "type of ground truth" used for performance verification. Given the nature of the device (image post-processing software) and the 510(k) pathway, performance verification likely involved:

• Technical validation: Comparing outputs of uWS-MR's features (e.g., image stitching, parameter maps, ROI measurements) against known good results, simulated data, or outputs from the predicate devices.
• Functional testing: Ensuring features operate as intended (e.g., if a rotation function rotates the image correctly).
  Pathology or outcomes data are typically used for diagnostic devices with novel clinical claims, which is not the primary focus here.

8. Sample Size for the Training Set

The concept of a "training set" typically applies to machine learning or AI models that learn from data. While the uWS-MR is post-processing software, and could potentially incorporate AI elements (though not explicitly stated beyond general "post-processing"), the document does not mention a training set size. This strongly suggests that a machine learning or deep learning model with a distinct training phase, as commonly understood, was not a primary component evaluated in this filing or, if present, its training data details were not part of this summary.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned (see point 8), the establishment of its ground truth is not applicable and therefore not described in the document.

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Prism Acquire® / Prism Process® software is used in conjunction with a Magnetic Resonance scanner to acquire and process blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and other MRI data sets. Prism View® software provides visualization of anatomical with functional and physiologic imaging data sets.

Prism Acquire presents a scripted series of synchronized visual and/or auditory stimuli and/or cognitive/motor tasks to the patient being scanned. The patient's responses and image data from the MRI scanner are stored for use by Prism Process, which performs post-processing for quality control and subsequent viewing of fMRI and other data. These applications can also be used to assist in scripted data acquisition and post-processing of other anatomical, functional and physiologic MR imagery including magnetic resonance spectroscopy (MRS), MR perfusion, and MR diffusion.

Prism View provides both analysis and viewing capabilities that promote the integration of anatomical with physiologic and functional imaging data sets including blood oxygen level dependent (BOLD) fMRI, magnetic resonance spectroscopy (MRS), MR perfusion, and MR diffusion including diffusion tensor imaging (DTI).

The integration of these data, when interpreted by a trained physician, yields information that may assist in the diagnosis of central nervous system pathology and the planning and monitoring of medical treatments.

Prism Acquire provides a scripted approach to performing fMRI and other imaging studies. Prism Process performs post-processing and quality assurance of fMRI and other imaging data sets. The processed data is prepared for report generation utilizing the Prism View product, supporting the visualization and manipulation of clinical imagery of multiple kinds. It provides a flexible set of display, analysis, and export options for utilizing the imagery relationships.

These applications may communicate in the healthcare IT environment via Prism Flow®, server-based software facilitating DICOM communications, authorization/authentication, audit logging, and other infrastructure functions.

The provided 510(k) summary for Prism Acquire®, Prism Process®, and Prism View® does not contain information about specific acceptance criteria or a detailed study proving the device meets particular performance metrics.

However, based on the available information, here's what can be extracted and what is explicitly not mentioned:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document states that "FDA has not established special controls or performance standards for this device." Therefore, the primary "acceptance criteria" appear to hinge on substantial equivalence to predicate devices and general software verification and validation. Specific quantitative performance metrics (e.g., sensitivity, specificity, accuracy) are not reported.

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided in the document.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and effect size

This information is not provided in the document. The document focuses on technological and safety equivalence to predicate devices, not on comparative effectiveness studies involving human readers.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

This information is not provided in the document. The general statement "Software verification and validation was conducted to confirm proper function of the device's features" is broad and does not detail specific standalone performance studies.

7. The Type of Ground Truth Used

This information is not provided in the document. Since specific performance metrics are not given, the type of ground truth against which those metrics would be compared is also absent. The device's output is intended to "assist in the diagnosis of central nervous system pathology and the planning and monitoring of medical treatments" when "interpreted by a trained physician," implying clinical interpretation as the ultimate reference, but how this translates to a ground truth for device performance is not detailed.

8. The Sample Size for the Training Set

This information is not provided in the document.

9. How the Ground Truth for the Training Set was Established

This information is not provided in the document.

Summary of Study Details Provided:

The document primarily relies on:

• Substantial Equivalence: The core of the performance "proof" is that Prism Acquire, Process, and View are technologically and safely equivalent to their own previous versions (BrainAcquireRx™, BrainProcessRx™, BrainViewRx™) and to other legally marketed predicate devices (Syngo® Multimodality Workstation, Advantage Windows with FuncTool Option).
• Software Verification and Validation: A general statement is made that "Software verification and validation was conducted to confirm proper function of the device's features." This typically covers testing for functionality, reliability, and security, but specific details or quantitative results are not included in this summary.

This type of 510(k) submission often focuses on demonstrating that the new device is as safe and effective as a legally marketed predicate device, rather than providing extensive de novo clinical trial data with detailed performance metrics.

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